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Improve the Hunger Games search algorithm to optimize the GoogleNet model

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  • Yanqiu Li
  • Shizheng Qu
  • Huan Liu

Abstract

The setting of parameter values will directly affect the performance of the neural network, and the manual parameter tuning speed is slow, and it is difficult to find the optimal combination of parameters. Based on this, this paper applies the improved Hunger Games search algorithm to find the optimal value of neural network parameters adaptively, and proposes an ATHGS-GoogleNet model. Firstly, adaptive weights and chaos mapping were integrated into the hunger search algorithm to construct a new algorithm, ATHGS. Secondly, the improved ATHGS algorithm was used to optimize the parameters of GoogleNet to construct a new model, ATHGS-GoogleNet. Finally, in order to verify the effectiveness of the proposed algorithm ATHGS and the model ATHGS-GoogleNet, a comparative experiment was set up. Experimental results show that the proposed algorithm ATHGS shows the best optimization performance in the three engineering experimental designs, and the accuracy of the proposed model ATHGS-GoogleNet reaches 98.1%, the sensitivity reaches 100%, and the precision reaches 99.5%.

Suggested Citation

  • Yanqiu Li & Shizheng Qu & Huan Liu, 2024. "Improve the Hunger Games search algorithm to optimize the GoogleNet model," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-20, August.
    • Handle: RePEc:plo:pone00:0305653
    DOI: 10.1371/journal.pone.0305653
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    References listed on IDEAS

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    1. Xionghua Huang & Tiaojun Zeng & MinSong Li & Ching-Feng Wen, 2022. "A Particle Swarm Optimization Algorithm with Gradient Perturbation and Binary Tree Depth First Search Strategy," Journal of Mathematics, Hindawi, vol. 2022, pages 1-13, November.
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